Introduction

This blog post is about personalizing the aesthetics of ggplot2 figures in R. I became interested in this topic when regularly completing assignments in R. I wanted my figures to look nice, and I enjoy visually appealing colors and shapes.

Data visualization is highly valuable: it allows audiences to gain a better understanding of data. Whether presenting data for a casual school environment or a more formal work environment, there are great ways to improve figures for any audience.

Overview

I begin by discussing labeling elements using labs. Then, I move to customizing text elements with theme and element_text. After, I discuss ways to alter the plot styles and characteristics with theme, element_rect, and element_line. Next, I demonstrate customizing scatter plots and bar charts. Finally, I show the animation of ggplot2 figures

Altering text using labs

Here is a relatively simple scatter plot using the built-in mtcars dataset.

library(ggplot2) # Load ggplot2 package 

figure1 = 
  ggplot(data = mtcars, # data
         aes(x = wt, # x-axis, weight
             y= mpg)) + # y-axis, miles per gallon
  geom_point() # scatter plot

figure1 # print figure

It is difficult to tell what this graph is about by looking at it. There is no title, and the axis labels are a bit unclear. In order to clarify, use labs to add a title, an x-axis label, and a y-axis label.

Arguments of labs include:

It is important to note that labs can be labeled for future use.

labs1 = 
  labs(x ="Car Weight (1000 lbs)", # x-axis label
       y ="Miles per U.S. gallon", # y-axis label
       title = "Figure 1: Miles per Gallon vs Weight") # plot title
figure1 + labs1

The graph looks much better with the labels. Now, it is clear what the plot is about. I am moving on to theme elements to demonstrate further customization options.

Label characteristics

We can change elements of the labels using element_text through theme

The arguments of theme demonstrated here include:

We can also use element_text to apply arguments to all text in the plot.

The arguments of element_text are as follows:

Demonstration

figure1 + # Original figure
  
  labs1 + # Labels of figure
  
  theme(   # Theme function
       plot.title =   # Plot title customizations 
          element_text(family = "Comic Sans MS",
                       face = "bold",
                       color = "red",
                       hjust = 0.5, # Horizontal adjust - title to center 
                       size = 14), # Font size
        
        axis.title.x = # x-axis label customization
          element_text(family = "Times New Roman",
                       face = "italic",
                       color = "blue",
                       size = 11), # Font size
        
        axis.title.y = # y-axis label customization
          element_text(family = "Courier",
                       face = "plain",
                       color = "green",
                       size = 13), # Font size
    
        axis.text.x =  # x-axis text customization
          element_text(color = "orange", # font color
                       size = 12,
                       angle = 330), 
        
        axis.text.y = # y-axis text customization
          element_text(color = "pink", 
                       size = 10,
                       angle = 90)
       ) # End of theme function

This plot is not visually appealing, but the varying colors and fonts help demonstrate the different pieces that can be altered.

Create reproducible theme

Themes can also be labeled for future use, so it is only necessary to include the code for the theme once. This is not only visually cleaner, but also less work. Below is the code that I use to alter the labels on my own graphs. I adjust the plot title to the center, customize the font sizes, and alter the margins to allow for more space.

# Label theme to easily add to graphs
mytheme = 
 theme( # Theme function
  
       plot.title = # Plot title 
          element_text(hjust = 0.5, # Horizontal adjust - title to center 
                       size = 14, # Font size
                       margin = margin(t = 10, b = 10)), # Top & bottom margins
        
        axis.title = # Axis labels customization
          element_text(size = 12, 
                       margin = margin(t = 12, r = 12, # Top & right margins
                                       b = 12, l = 12)), # Bottom & left margins
        
        axis.text.x =  # x-axis text customization
          element_text(color = "black", # Font color
                       size = 10, 
                       margin = margin(t = 5, b = 5)), 
        
        axis.text.y = # y-axis text customization
          element_text(color = "black", 
                       size = 10,
                       margin = margin(r = 5, l = 5))
       ) # End of theme function

Label Results

You can see the results of this theme. Below, I add the original figure, the saved labs, and the saved theme.

figure1_2 =
  figure1 + 
  labs1 + 
  mytheme

figure1_2

It looks much nicer than the previous graphs.

Plot Styles

The arguments of theme demonstrated here include:

Beyond element_text, the functions element_rect and element_line also contribute to figure aesthetics.

Available line types for element_line: “blank”, “solid”, “dashed”, “dotted”, “dotdash”, “longdash”, “twodash”

Example

figure1_3 =
  figure1 +
  labs1 +
  theme( # Theme function
        plot.background = 
          element_rect(fill = "skyblue"), # Fill of entire plot background
        
        panel.background = 
          element_rect(fill = "lightcyan"), # Fill of plotting area background
        
         panel.grid.minor = 
          element_line(linetype = "dashed",  # Type of Line
                       color = "darkgreen"), # Color of Line
        
        panel.grid.major = 
          element_line(linetype = "solid",
                       color = "skyblue"),
        
        axis.line = 
          element_line(size = 2, # Size of Line
                       linetype = "twodash",
                       color = "grey80"),
        
        axis.ticks = 
          element_line(size = 3, 
                       color = "orange")
        ) # End of theme function

figure1_3

This plot is not visually appealing, but the varying colors and patterns help demonstrate the different pieces that can be altered.

Final theme and results

The theme can be updated by adding further theme elements to the current saved theme. I have included the theme elements I frequently use.

mytheme =
  mytheme +
  theme(plot.background = 
          element_rect(fill = "lavender"),
        
        panel.background = 
          element_rect(fill = "white"),
        
         panel.grid.minor = 
          element_line(linetype = "solid", 
                       color = "grey92"),
        
        panel.grid.major = 
          element_line(linetype = "solid",
                       color = "grey90"),
        
        axis.line = 
          element_line(size = .3, 
                       linetype = "solid",
                       color = "grey50"),
        
        axis.ticks =
          element_line(color = "black", 
                       size = 1))

Here is the result of combining the original figure, the text, and the saved theme.

fig1 = 
  figure1 + 
  labs1 + 
  mytheme

fig1

This final graph is more visually appealing than previous plots.

Comparison

Below is a comparison of the four plots made using the patchwork package.

library(patchwork)
figure1 + figure1_2 + figure1_3 + fig1 +  plot_layout(nrow = 2)

Scatter plots

In a scatter plot, it’s possible to change the color of the points based on a certain factor by using geom_point (then color = as.factor(variable). This can help demonstrate further information about the data.

geom_point can also be used to change the size and shape of the points.

I’m going to further customize the most recent plot by changing the color of the points to represent the number of gears a car has.

Example

fig1_2 = 
  ggplot(data = mtcars, # data
         aes(x = wt, # x-axis, weight
             y= mpg)) + # y-axis, miles per gallon
  
  geom_point(aes(color = as.factor(gear)), # Scatter plot with gear as factor
             size = 2.5, # Size of points
             shape = 8) + # Shape of points
  
  scale_color_manual(values = # Change colors of values
                       c("3" = "aquamarine3", 
                         "4" = "mediumpurple",
                         "5" = "orchid3"),
                     name = "# of Gears") + # Legend Title  
  
  mytheme + # Add previous theme 
  
  labs1 # Add labels

fig1_2

The teal points represent cars with 3 gears, the purple points represent cars with 4 gears, and the pink points represent cars with five gears.

Comparison

Below is a comparison of the previously finalized plot and the updated version with gear as a factor.

fig1 + fig1_2 + plot_layout(ncol = 2)

Bar chart

Bar charts (geom_bar) can be altered much like scattered plots.

Legend customization

Theme elements relating to legends:

  • legend.background = background of legend (element_rect())

  • legend.position = position of legends (“none”, “left”, “right”, “bottom”, “top”)

  • legend.direction = layout of items in legends (“horizontal” or “vertical”)

  • legend.margin = the margin around each legend (margin())

  • legend.title = title of legend (element_text())

  • legend.title.align = alignment of legend title (from 0 (left) to 1 (right))

  • legend.text = legend item labels (element_text())

  • legend.text.align = alignment of legend labels (from 0 (left) to 1 (right))

Example

Below is a basic bar chart demonstrating the frequency of each level of gears in the dataset (3, 4, and 5). The variable for the fill of geom_bar can’t consist of just numbers, so I rename them using mutate from the dplyr package. Then, gears is a factor representing the numerical values in gear.

library(dplyr) # Use dplyr for mutate function

mtcars =
  mutate(   # Mutate function
    mtcars, # Data
    gears = # Mutatation of gear
       ifelse(gear == 3, "(3)", # If 'gear' is 3, value in 'gears' is '(3)' 
       ifelse(gear == 4, "(4)", # If 'gear' is 4, value in 'gears' is '(4)'
                         "(5)")) # If neither of these is true, value in 'gears' is '(5)'
         ) 
figure2 = 
  ggplot(mtcars, # Data
         aes(x = gear)) + # X is gear as a factor
  
  geom_bar( # Bar chart function
    aes(fill = gears) # Colors of bar chart are varying levels of gears
    )  # End of bar chart function

figure2

Result

I improve the previous bar chart by adding axis and title labels, manually choosing the colors of the bars using scale_fill_manual, removing the legend using theme and legend_position, and finally adding the precreated theme.

# Create labs

labs2 = 
  labs(x ="Number of Gears", # x-axis label
       y ="Count", # y-axis label
       title = "Figure 2: Count of Number of Gears") # Title


# Add 
fig2 = 
  figure2 + 
  
  scale_fill_manual(values =  # Chnange colors of bars manually
                      c("aquamarine3", "mediumpurple", "orchid3")) +
  
  labs2 +
  
  theme(legend.position="none") + # remove legend
  
  mytheme # Add theme

fig2

Animating ggplot graphs

Animation can be added to pre-xisting ggplot figures with the fuction ggplotly from the package plotly.

Hover over the points to find out more information. Using the option in the top right corner, you can also download plots as a png, zoom, select, and compare data on hover.

Figure 1 animated

Here is an animated version of figure 1.

library(plotly) # Load plotly package

ggplotly(fig1_2) # Animate figure 1

Figure 2 animated

ggplotly(fig2)

Conclusion

There are many ways to customize ggplot2 figures. I have barely scratched the surface in this post, but this is a good start for those that are unfamiliar.

Other resources

Here are some other resources for personalizing the aesthics of a ggplot2 figure.